为实现田间环境下对玉米苗和杂草的高精度实时检测,本文提出一种融合带色彩恢复的多尺度视网膜(Multi-scale retinex with color restoration,MSRCR)增强算法的改进YOLOv4tiny模型。首先,针对田间环境的图像特点采用MSRCR算法进行图像...为实现田间环境下对玉米苗和杂草的高精度实时检测,本文提出一种融合带色彩恢复的多尺度视网膜(Multi-scale retinex with color restoration,MSRCR)增强算法的改进YOLOv4tiny模型。首先,针对田间环境的图像特点采用MSRCR算法进行图像特征增强预处理,提高图像的对比度和细节质量;然后使用Mosaic在线数据增强方式,丰富目标检测背景,提高训练效率和小目标的检测精度;最后对YOLOv4tiny模型使用K-means++聚类算法进行先验框聚类分析和通道剪枝处理。改进和简化后的模型总参数量降低了45.3%,模型占用内存减少了45.8%,平均精度均值(Mean average precision,mAP)提高了2.5个百分点,在Jetson Nano嵌入式平台上平均检测帧耗时减少了22.4%。本文提出的PruneYOLOv4tiny模型与Faster RCNN、YOLOv3tiny、YOLOv43种常用的目标检测模型进行比较,结果表明:PruneYOLOv4tiny的mAP为96.6%,分别比Faster RCNN和YOLOv3tiny高22.1个百分点和3.6个百分点,比YOLOv4低1.2个百分点;模型占用内存为12.2 MB,是Faster RCNN的3.4%,YOLOv3tiny的36.9%,YOLOv4的5%;在Jetson Nano嵌入式平台上平均检测帧耗时为131 ms,分别是YOLOv3tiny和YOLOv4模型的32.1%和7.6%。可知本文提出的优化方法在模型占用内存、检测耗时和检测精度等方面优于其他常用目标检测算法,能够为硬件资源有限的田间精准除草的系统提供可行的实时杂草识别方法。展开更多
针对TINY YOLO车辆检测算法计算量过大,且在小型嵌入式系统中难以达到实时检测要求的问题。利用小型Zynq So C系统的架构优势以及TINY YOLO的网络权值中存在大量接近零的权值参数这一特点,提出硬件并行加速的改进算法,称为浓缩小型深度...针对TINY YOLO车辆检测算法计算量过大,且在小型嵌入式系统中难以达到实时检测要求的问题。利用小型Zynq So C系统的架构优势以及TINY YOLO的网络权值中存在大量接近零的权值参数这一特点,提出硬件并行加速的改进算法,称为浓缩小型深度网络(Xerantic-TINY YOLO,X-TINY YOLO)车辆检测算法。首先对TINY YOLO中网络结构进行压缩;其次采用高效多级流水线、流水线内全并行的方式对卷积计算部分进行算法加速;最后提出与网络结构相配合的数据切割和传输方案。实验结果表明,X-TINY YOLO仅消耗50%的片内硬件资源,可在相对于GPU和CPU性价比更高更适合嵌入式场景的Zynq So C系统上实现,且其检测速度达到24帧/s,满足车辆检测的实时性要求。展开更多
This study investigates and quantifies some possible sources affecting the position of impact points of small caliber spin-stabilized projectiles(such as 12.7 mm bullets).A comparative experiment utilizing the control...This study investigates and quantifies some possible sources affecting the position of impact points of small caliber spin-stabilized projectiles(such as 12.7 mm bullets).A comparative experiment utilizing the control variable method was designed to figure out the influence of tiny eccentric centroids on the projectiles.The study critically analyzes data obtained from characteristic parameter measurements and precision trials.It also combines Sobol’s algorithm with an artificial intelligence algorithmdAdaptive Neuro-Fuzzy Inference Systems(ANFIS)ein order to conduct global sensitivity analysis and determine which parameters were most influential.The results indicate that the impact points of projectiles with an entry angle of 0°deflected to the left to that of projectiles with an entry angle of 90°.The difference of the mean coordinates of impact points was about 12.61 cm at a target range of 200 m.Variance analysis indicated that the entry angleei.e.the initial position of mass eccentricityehad a notable influence.After global sensitivity analysis,the significance of the effect of mass eccentricity was confirmed again and the most influential factors were determined to be the axial moment and transverse moment of inertia(Izz Iyy),the mass of a projectile(m),the distance between nose and center of mass along the symmetry axis for a projectile(Lm),and the eccentric distance of the centroid(Lr).The results imply that the control scheme by means of modifying mass center(moving mass or mass eccentricity)is promising for designing small-caliber spin-stabilized projectiles.展开更多
文摘为实现田间环境下对玉米苗和杂草的高精度实时检测,本文提出一种融合带色彩恢复的多尺度视网膜(Multi-scale retinex with color restoration,MSRCR)增强算法的改进YOLOv4tiny模型。首先,针对田间环境的图像特点采用MSRCR算法进行图像特征增强预处理,提高图像的对比度和细节质量;然后使用Mosaic在线数据增强方式,丰富目标检测背景,提高训练效率和小目标的检测精度;最后对YOLOv4tiny模型使用K-means++聚类算法进行先验框聚类分析和通道剪枝处理。改进和简化后的模型总参数量降低了45.3%,模型占用内存减少了45.8%,平均精度均值(Mean average precision,mAP)提高了2.5个百分点,在Jetson Nano嵌入式平台上平均检测帧耗时减少了22.4%。本文提出的PruneYOLOv4tiny模型与Faster RCNN、YOLOv3tiny、YOLOv43种常用的目标检测模型进行比较,结果表明:PruneYOLOv4tiny的mAP为96.6%,分别比Faster RCNN和YOLOv3tiny高22.1个百分点和3.6个百分点,比YOLOv4低1.2个百分点;模型占用内存为12.2 MB,是Faster RCNN的3.4%,YOLOv3tiny的36.9%,YOLOv4的5%;在Jetson Nano嵌入式平台上平均检测帧耗时为131 ms,分别是YOLOv3tiny和YOLOv4模型的32.1%和7.6%。可知本文提出的优化方法在模型占用内存、检测耗时和检测精度等方面优于其他常用目标检测算法,能够为硬件资源有限的田间精准除草的系统提供可行的实时杂草识别方法。
基金supported by the Fundamental Research Funds for the Central Universities,China(grant no.30918012203)the Foundation of National Laboratory,China(grant no.JCKYS2019209C001)。
文摘This study investigates and quantifies some possible sources affecting the position of impact points of small caliber spin-stabilized projectiles(such as 12.7 mm bullets).A comparative experiment utilizing the control variable method was designed to figure out the influence of tiny eccentric centroids on the projectiles.The study critically analyzes data obtained from characteristic parameter measurements and precision trials.It also combines Sobol’s algorithm with an artificial intelligence algorithmdAdaptive Neuro-Fuzzy Inference Systems(ANFIS)ein order to conduct global sensitivity analysis and determine which parameters were most influential.The results indicate that the impact points of projectiles with an entry angle of 0°deflected to the left to that of projectiles with an entry angle of 90°.The difference of the mean coordinates of impact points was about 12.61 cm at a target range of 200 m.Variance analysis indicated that the entry angleei.e.the initial position of mass eccentricityehad a notable influence.After global sensitivity analysis,the significance of the effect of mass eccentricity was confirmed again and the most influential factors were determined to be the axial moment and transverse moment of inertia(Izz Iyy),the mass of a projectile(m),the distance between nose and center of mass along the symmetry axis for a projectile(Lm),and the eccentric distance of the centroid(Lr).The results imply that the control scheme by means of modifying mass center(moving mass or mass eccentricity)is promising for designing small-caliber spin-stabilized projectiles.
